Visualizing Recovery of Cognitive Function in Stroke
Bibliographic record
Abstract
Hippocrates (460-377 BC) first described stroke over 2400 years ago. Stroke is the 4th leading cause of death in Canada (3rd in the USA) and the primary cause of permanent motor and cognitive disability. The majority of strokes are ischemic. The extent of cerebral dysfunction and thus the severity of stroke are based on the location, severity and duration of ischemia. Stroke management and prognosis encompass early recognition of the onset of stroke and post-stroke determination of the extent of brain injury aided by clinical stroke scores and diffusion-weighted imaging. Cognitive domains most likely to be affected following stroke are memory, orientation, language, attention and executive function. While the vast majority of functional recovery occurs within the first 3 months post-stroke, the neural mechanisms promoting recovery are not well understood. Investigations into the neural plasticity of brain areas after a lesion demonstrate that the adult brain can be shaped by environmental inputs, such as rehabilitation techniques. Many rehabilitation techniques are actively being pursued, including brain-computer interfaces providing sophisticated methods for detecting rehabilitation-associated changes in cerebral physiology. The success of such strategies visualized with functional magnetic resonance imaging and positron emission tomography may provide an objective complement to clinical evaluations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".